I deployed Hermes Agent to my Hostinger VPS with the one-click Docker Compose template. The build log said Project deployed successfully. It lied — the container came up, but its HTTPS was broken and its admin dashboard was published to the public internet over plain HTTP.
This is the walkthrough of what the template actually gives you, the three defects I found on my own box, and the compose file I run now.
If you already run OpenTelemetry for your services, you don't need a new telemetry stack for LLMs. You need the same tracer, the same collector, the same Grafana dashboard — pointed at a new model of work. The recent GenAI semantic conventions let spans, metrics, and logs describe what an LLM actually did: which model, which prompt, how many tokens, how long, what it cost, and which tool it called.
A quant team has two completely different problems to solve. One is research: lots of analysts, lots of notebooks, lots of Parquet, slow human-in-the-loop iteration. The other is production: a small trading desk, kernel-bypass networking, microsecond budgets, almost no humans. The mistake is trying to run both on the same infrastructure. The right answer is two stacks, one boundary.
Platform engineering spent years turning tribal knowledge into YAML: catalog descriptors, scaffolder templates, scorecards, policies, and deployment workflows. Now LLMs are becoming the conversational layer over that machinery. The useful shift isn't replacing YAML with chat. It's giving developers a safer way to discover and invoke the platform capabilities already encoded underneath.
A fresh Kubernetes cluster has no idea your nodes have GPUs. kubectl describe node shows CPU, memory, and pods — nothing else. To make a pod request a GPU you need a driver, a container runtime hook, and a device plugin advertising the hardware to the scheduler, all version-matched across every GPU node. Do it by hand and you'll re-do it on every kernel bump. This post wires it up the way you actually want — one operator — and works on any Kubernetes, not a specific vendor's distro.
MIG carves a GPU into hardware-isolated slices. Time-slicing oversubscribes each slice so more pods can share it. Stack them and one physical GPU advertises far more schedulable units than it has silicon — useful when you have more workloads than GPUs and most of them sit idle. Here's the exact values.yaml, wired into the kommander-applications GPU Operator 26.3.0 app1, and the labels that switch a node between layouts.
Your cluster routes every packet through iptables, and at a few thousand Services that chain becomes a linear scan the kernel walks per connection. Cilium throws that out and runs networking, policy, and observability as eBPF programs in the kernel instead — no sidecars, no iptables, no agent in the data path. This post walks the whole stack: how a packet even finds another pod, installing Cilium as a kube-proxy replacement, watching real traffic with Hubble, locking the cluster down with identity-based policy, stretching it across clusters with Cluster Mesh, and dropping the service-mesh sidecars — all copy-paste.
Use case: ship Kubernetes ingress logs off the cluster into a self-managed Elastic Stack on Ubuntu servers, parse them, and keep them searchable for 90 days without blowing the disk budget. The Elastic Stack handles this cleanly, and the 2026 build leans on four current features that older tutorials skip: LogsDB, ILM, data streams, and a Kubernetes-native shipper.
Run Kong so that a database outage can't take your APIs down, every config change goes through a merge request, and one gateway serves both public and internal traffic without mixing them. That's hybrid mode plus GitOps on Kubernetes. This post builds it on NKP (Nutanix Kubernetes Platform), with a companion project you can run on a laptop first.
Most teams invent their Git strategy as they go. One developer starts tagging releases. Another uses branches. A third creates a hotfix branch and never deletes it. By the time you need to patch a six-month-old release, nobody remembers what release-final-v2 means or where hotfixes actually landed.
A clear branching strategy prevents this. This post covers a battle-tested Git workflow built around two parallel approaches — version control with branches and version control with tags — and tells you exactly when to use each.